Method for calling for preemptive maintenance and for equipment failure prevention

ABSTRACT

A method for operating digital electronic appliance that empanels several different artificial intelligence (AI) classification technologies into a “jury” uses combinational digital logic to render “verdicts” about the need for service and impending equipment failures of the machines they monitor. Networks can be used to forward signals from remote locations to a centralized appliance that may be plugged as a module into a server. The appliance outputs can also be communicated over networks to servers that will muster appropriate maintenance personnel who are forewarned as to the nature of the trouble

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods for protecting groups ofdigital electronic appliances used collectively for monitoring theoperation of machines and for issuing predictions, warnings and callsfor preventative maintenance and equipment failure interventions, andmore particularly to methods that use computer data processing systemsto empanel several artificial intelligence (AI) classificationtechnologies into a “jury” that renders “verdicts” about the need forservice and impending equipment failures.

2. Background

Digital signal processors (DSP) implemented as specialized semiconductorintellectual property (SIP) cores have circuit architectures that havebeen optimized for the operational needs of digital signal processing.DSP's are used to measure, filter and/or compress continuous real-worldanalog signals. Many general-purpose microprocessors can also executedigital signal processing algorithms successfully, but dedicated DSP'susually have better power efficiency thus they are more suitable inportable devices such as mobile phones because of power consumptionconstraints. DSP's frequently use special memory architectures that areable to fetch multiple data and/or instructions at the same time.

Xilinx defines Field Programmable Gate Arrays (FPGA's) as semiconductordevices that are based around a matrix of configurable logic blocks(CLB's) connected via programmable interconnects. FPGA's can bereprogrammed to desired application or functionality requirements aftermanufacturing. This feature distinguishes FPGA's from ApplicationSpecific Integrated Circuits (ASIC's), which are custom manufactured forspecific design tasks. Although one-time programmable (OTP) FPGA's areavailable, the dominant types today are based on static random accessmemory (SRAM) which can be reprogrammed circuit by circuit as the designevolves.

ASIC's and FPGA's have different value propositions, and so the choicesmust be evaluated. FPGA's once were selected for their lowerspeed/complexity/volume designs. But modern FPGA's now push theso-called 500-MHz performance barrier and are benefiting from logicdensity increases, embedded processors, DSP blocks, clocking, andhigh-speed serial, at ever lower price points.

Another is to employ a general purpose computer with its inputs,outputs, and processing specially adapted to operate in the deviceenvironment and to execute its programming in the ways described herein.Such may be the most flexible way to implement these embodiments, butthe costs to do so may be excessive, especially if only a small portionof the computer's capabilities and resources are employed.

SUMMARY OF THE INVENTION

Briefly, digital electronic appliance embodiments of the presentinvention empanel several different artificial intelligence (AI)classification technologies into a “jury” that uses combinationaldigital logic to render “verdicts” about the need for service andimpending equipment failures of the machines they monitor. Networks canbe used to forward signals from remote locations to a centralizedappliance that may be plugged as a module into a server. Or, theappliance outputs can be communicated over networks to servers that willmuster appropriate maintenance personnel who are forewarned as to thenature of the trouble.

The above and still further objects, features, and advantages of thepresent invention will become apparent upon consideration of thefollowing detailed description of specific embodiments thereof,especially when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is functional block diagram an appliance for monitoring a machineand for predicting and reporting its need for service or the probabilityof an imminent failure;

FIG. 2 is functional block diagram of a variety of classificationtechnologies arranged as “jurors” in a jury panel to receive the sameevidence from a machine, but then use their own respective talents andhardwiring to arrive at independent decisions output as “votes”;

FIG. 3 is a flowchart diagram illustrating a method embodiment of thepresent invention for monitoring the operation of machines and forissuing calls for preventative maintenance and predictions of equipmentfailures; and

FIG. 4 is a functional block diagram of a digital electronic applicationspecific integrated circuit (ASIC) embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 represents a group of electronic appliances for monitoring theoperation of machines or devices, and for issuing predictions, warningsand calls for preventative maintenance and equipment failures, all in anembodiment of the present invention, and such is referred to herein bythe general reference numeral 100. Monitoring appliance 100 is attachedto a machine 102 that uses inputs 104 to produce outputs 106. Forexample, if machine 102 were a diesel electric generator, the primaryinputs 104 would be diesel fuel and the primary output 106 would beelectrical power, e.g., 440 VAC three phase. Secondary inputs 104 couldbe motor oil for lubrication and water for cooling. Secondary outputs106 could be a battery charging 24 VCD output, a local 110 VAC utilityfor lighting.

Machine 102 could be anything from a washing machine, to an automobile,to a utility power station, to a computer mainframe, to a heatingfurnace, to a ship, to a network server. These would be “things” in theemerging Internet of Things.

Monitoring appliance 100 uses transducers to continuously measurevarious parameters associated with machine 102. In the example of FIG.1, an input transducer 108 provides an electrical signal 109 inproportion to some input flow, e.g., 4-20 milliamps to represent 0-10milliliters per minute or 0-100 liters of diesel per hour. An outputtransducer 110 provides an electrical signal 111 in proportion to someoutput flow, e.g., 4-20 ma to represent 0-1000 volts or 0-100 kilowatthours.

Typical equipment, like machine 102, will produce indirect, inadvertent,or consequential symptoms, clues, noise, emissions, evidence, and otheroutputs 112-121 that can be measured and monitored by various types ofcorresponding transducers 124-133, as in Table I.

TABLE parameter transducer measurement units heat 124 temperatureappearance 125 video sound 126 audio vibration 127 accelerometer vapors128 smoke, carbon monoxide chemistry 129 pH acidity radio 130radio/magnetic emissions electrical 131 electrical noise speed 132 RPM,MPH, % of rated weight 133 pounds, kilograms, mass

Experienced troubleshooters who become familiar with other devices likemachine 102 will, over time, get to know the symptoms of an impendingfailure or an already developed one. Or when service is really needed,as opposed to a scheduled time coming near. This experience can be“canned” for use by automation and artificial intelligence.

Internal combustion engines are well known to produce rattles,vibrations, and unusual noises when something has gone wrong. Cars tooproduce symptoms like loss of power, hard starting, excessive fuelconsumption, gas bubbles in the coolant, high operating temperatures,dead batteries, taillight malfunctions, etc. Very often one componentwill trigger problems with or damage to other components.

A data input subsystem 140 monitors and reports the many readings,measurements, and information collected from transducers 108, 110, and124-133. The products of the transducers will generally produce expectedvalues within the specifications of machine 102. Each individual machine102 will settle on some variations from average or mean values thatbecome its norms. Different kinds of devices 102 will express differentsuites of parameters, and not every or all parameters will be harbingersthat can be useful in these embodiments. Those represented here are justtypical. Some parameters can be computed from one or more of themeasured parameters, e.g., totalized hours of operation, or efficiencylike miles per gallon.

The measured and computed attributes of machine 102 are numerous and canbe used to render a “picture” of machine 102 each instant and “movies”over spans of time. These attributes are collected and distributed intoparallel attribute sets 142, wherein each set 142 repeats all the sameattributes derived from all the transducers.

Sometimes it is necessary to apply a test or a combination of teststimulus 144 to the machine 102 to get it to respond in a certain orhelpful way that can be observed and analyzed. For example, a teststimulus 144 could be controls to do a hard reset, or a stop/startcycle.

In the virtual world of a computer, the parallel attribute sets 142replace the actual physical machine 102 and act as abstractions of it.If the parallel attribute sets 142 are comprehensive, then a complete“picture” can be had. In one sense, the actual physical machine 102becomes completely intangible. The parallel attribute sets 142 dissolveinto digital or analog values that data processing systems canmanipulate and analyze. Machine 102 abstracts a virtual “entity” that isfully represented by its attributes.

A parallel panel or “jury” of classification engines (A-G) 151-157 eachapply different techniques and methods to analyze, interpret, andscrutinize identical parallel attribute sets 142. The objective is toget a variety of perspectives using a variety of classicalclassification technologies to “decide” if the inputs they see arenormal or not. Here, the present combination existing at the instant onthe inputs is assigned by each classification engine (A-G) 151-157 toone of several categories.

The input data for any classification task is a collection of records.Each record, instance, for example, is characterized by a tuple (x,y),where x is the attribute set and y is a special attribute, designated asthe class label, category or target attribute. For example, vertebratescan be classified into mammals, birds, fish, reptiles, and amphibians.The attribute set includes properties and abilities. The attribute setscan be continuous. The class label, in contrast, must be a discreteattribute. This is a key characteristic that distinguishesclassification from regression, where y is a continuous attribute in apredictive modeling task.

A smart agent 158 receives the same inputs as classification engines(A-G) 151-157, but runs a number of short-term, long-term, and recursiveprofiles 159 to characterize what is routine and what seems to be anexcursion from average operations.

A weighting matrix 160 acts as a judge presiding over the jury. Excepthere, the weighting matrix 160 can give more or less weight to each“vote” produced by a classification engine (A-G) 151-157 and smart agent158. A courtroom judge is not allowed to disassemble the verdict of acourtroom jury. Here, weighting matrix 160 receives tuning inputs 162 inthe form of a priori information, e.g., experience, training, memory,skills, events, extraneous data, news, or business directives toinfluence which classification engines (A-G) 151-157 are to be givenmore credence. The consequence of all this is to output a finding or“judgment” 164, e.g., normal, suspicious, or not normal. In the case ofa preventative maintenance application, nominal, service needed by partX for reason Y, or imminent failure or outright failure due to W.

FIG. 2 represents a machine assembly 200 in which a variety ofclassification technologies 202-204 are, conceptually speaking, arrangedas “jurors” in a jury panel 206 to receive the same monitor data(evidence) 208-210 from a machine 212. Much like jurors in a courtroomjury. Each juror 202-204 then each juror uses their own respectivetalents and hardwiring to arrive at independent decisions output as“votes” 214-219. But in this jury, the “verdict” the jurors deliver area prediction 215, 216, and 218, e.g., “failure” or “needs service”, andhow confident the prediction is, e.g., in a confidence score 215, 217,and 219, for example, ranging 0.00 to 1.00.

The development and construction of jurors and jury panels is more fullydisclosed in our co-pending application, U.S. patent application Ser.No. 14/815,848 filed Jul. 31, 2015, AUTOMATION TOOL DEVELOPMENT METHODFOR BUILDING COMPUTER FRAUD MANAGEMENT APPLICATIONS. Such isincorporated by reference herein, in full.

Many different classifier technologies and models have been developed byartificial intelligence experts over the years. Conventionally,classification models have been implemented as data structures orprograms in data processing systems that define parameters for theiralgorithms and control how the corresponding classification model willuse its inputs to classify a transaction, event, or parallel attributeset. Here, we use application specific digital logic to do the samething, only less expensively, more quickly, and more distallydistributed. The classifications can be simple, e.g., good/bad andrepresented by digital logic bits, 1/0. Or they can be a little moreinformative like in fraud detection and payment authorization systems,e.g., approved/suspicious/declined, for example with a binary codeddecimal on four bit lines, 0-0FH in hexadecimal notation.

Each type of traditional classification model is known to excel indifferent circumstances and therefore has different uses, e.g., neuralnetworks, decision trees, genetic algorithms, etc. It may not always beknown which is going to do well in any given situation, but time andexperience with them will reveal the facts. As a juror in a jury panel,and as a result of its hardwiring, each juror has a differentperspective, talents, education, training, experience, expectation,external pressures, etc. It will become evident that some jurors in thejury are better readers of the evidence, and a metarule arbiter 230 iswired to prefer or favor particular jurors 202-204.

Individual “votes” 214-219 can be ignored in confidence gates 232-234 iftheir respective confidences fall below some threshold 235-237. Eachconfidence value is independently determined by confidence gates232-234.

So, jurors 202-204 will attach their own confidence in a particular votethey output, and the metarule arbiter 230 can decide for itself if andhow much reliance should be placed on each juror 202-204. The metarulearbiter 230 is able to completely overrule the entire jury 206, much thesame way a trial judge can enter a “judgment notwithstanding theverdict” (JNOV, non obstante veredicto) in court. A meta-rules inputsetting 238 would be used to influence the findings.

Embodiments of the present invention use combinational digital logiccircuits to compose composite prediction outputs 240. The multipleartificial intelligence technologies embedded and including ourparticular smart-agents with real-time and long-term profiling, datamining, neural networks, business rules, fuzzy logic, case-basedreasoning, etc. Each technology independently returns its ownpredictions, confidence scores, and reason details. In general, neuralnetworks cannot be asked about their predictions and their scores.

Final scores are asserted proportional to the health of the equipment,and comments like “service required now” and a computed likelihood offuture failures. Otherwise, the equipment/device has no issue.

A variety of digital devices can be used to calculate a final score.

Flag settings in ordered rules are used to indicate how best to processeach technology's predictions into a composite conclusion. For example,a first rule for a neural network technology is given a threshold of0.75. A second rule for a data mining technology is given a threshold of0.7. A third rule for a case-based reasoning technology is given athreshold of 0.8. These initial threshold settings often are the resultsof experience or testing.

GIVEN FLAG SETTINGS prediction classifier assigned type AI technologythreshold all neural network 0.75 all data mining 0.7 allcase-based-reasoning 0.8

In one illustration, these three technologies provide a real-time set ofpredictions in parallel of 0.7, 0.8. and 0.4, respectively, for aparticular machine or device under surveillance.

EXAMPLE PREDICTIONS prediction classifier instant class AI technologyconfidence failure neural network 0.7 failure data mining 0.8 serviceneeded case-based-reasoning 0.4In the example, a first rule looks at the neural network technology'sconfidence of 0.7 and sees that in this instance the confidence is belowthe given threshold of 0.75, so this is dismissed and the nextprediction is considered. A second rule looks at a data miningtechnology's confidence of 0.8 and sees that in this instance it isabove the given threshold of 0.7, so this prediction is flagged to beincorporated in a composite output prediction. A third rule looks at thecase-based-reasoning technology's confidence of 0.4 and sees that inthis instance the confidence is below the given threshold of 0.8, and sothis one too is dismissed and any next predictions are considered.

It is also possible with the prediction class to associate a firsthigher threshold for future failure predictions, and a second lowerthreshold that triggers a call for equipment service or maintenance.

A winner-take-all method is used to group individual predictions byclasses. Each technology has its own weights, e.g., the one used when topredict a “risk of failure”, another one used to predict a “need forservice”. All such similar predictions are grouped together by summingtheir weighted confidences. The sum of the weighted confidences is thendivided by the sum of the weights used in order to obtain a finalconfidence score, e.g., ranging between 0.00 and 1.00.

Consider the following example:

WEIGHTS classifier weight: weight: AI technology failure need serviceneural network 2 2 data mining 1 1 case-based-reasoning 2 2

PREDICTIONS prediction classifier instant class AI technology confidencefailure neural network 0.7 failure data mining 0.8 service neededcase-based-reasoning 0.4In this example, two technologies are predicting a failure, so theircumulated weighted confidence is: 2*0.7+1*0.8, or 2.2. Only onetechnology is predicting “needs service”, so its weighted confidence issimply, 1*0.4, or 0.4. Since “2.2” is greater than “0.4”, the finalscore is scored as a risk of failure. The confidence is then normalizedby dividing it by the sum of the weights that where associated withfailure (2 and 1), so the final confidence is 2.2/(2+1), or 0.73.

It is possible to create models that can predict more than “risk offailure” or “needing service” categories. Business rules can be writtento combine the final scores of the several technologies.

Each business rule comprises the name of rule, and its conditions like“if the data mining confidence is greater than 75% then . . . ”.Conclusions are written like “ . . . then there is a risk of failurewith a confidence of 80%”. The kinds of “final score rules” that can bewritten depend on the application and the experts available. In anexample 1, “if data mining confidence is greater than 75%, and theequipment contains dangerous products, then . . . ”. In an example 2,“if the data mining prediction is risk of failure, and the case-basedreasoning prediction is “service needed” then . . . ”.

Testing the models allows their tuning. For example, if during a testingphase of a predictive model for data mining scores the class<<Service>>, with low number of errors, then a high weight is assignedto the service class for data mining. But if the neural networks do notcorrectly predict when service was needed, a lower weight is assignedfor the service class compared to the neural networks.

Each of the several technologies can have a weight used to predict “riskof failure”, and a different weight to predict “service required”. Morethan these two classes of “risk of failure” and “service required” arepossible.

This is more than one way to implement and embody the present inventioninto digital electronics hardware. A first is a completely customcircuit design with the circuits executed in a silicon integratedcircuit, e.g., in a so-called Application Specific Integrated Circuit(ASIC). Another is to employ a field-programmable gate array (FPGA) inwhich optional circuits are more or less permanently wired together bythe irreversible destruction of fuses or anti-fuses.

A “smart agent” by our definition is a data structure prearranged withina computer memory to epitomize an entity or thing in all its essentialsby the entity's attributes, history, and behaviors that are manifest,e.g., from specifications, programming, sensors, and transaction data.Each smart agent allows an electronic rendering of the entity or thingit is consigned to in several different and selectable dimensions. Eachsmart agent can be equipped with “action items” or a “job description”that boils down into clocking a series of steps in a finite statemachine (FSM), given various conditions being present in its data,sensors, programming, or other data solicited from other smart agents.Such finite state machine can issue output signals and triggers in eachstep to other smart agents. The computer processor resources necessaryto support such functionality are specially adapted to read/write thedata structures in memory, run the finite state machines, provide theinputs, and generate the outputs. In the field, such computer processorresources can be a shared mobile device, an embedded microcomputer, orbatch processor. A typical smartphone today represents all the sensor,processing, and communications hardware a typical smart agent wouldneed.

A smart agent can exist at some central location pooled with hundreds,thousands, or even millions of others that receive transaction recordsreporting the remote activities of the corresponding participants eachis assigned to follow. For example, inside a network server they caninteroperate and intercommunicate fairly freely and efficiently.

In the Internet-of-Things, the hundreds, thousands, or even millions ofparticipants can be widely dispersed and are each assigned and equippedwith a smart agent that is able to communicate with the others.Nowadays, that communication would rely on a wireless technology likeWiFi, Bluetooth, NFC, GSM, 4G, etc. Some wireless technologies canbreach long distances, others have the advantage of needing to beproximate or very close. That may help secure access to authorized(local) users in a very simple way hard to subvert.

Participants and entities in general are describable by theirattributes. Even in widely diverse groups. In one sense, suchparticipants and entities are nothing more than the sum of theirattributes. Groups too. And attributes too are describable by theirattributes. For example, if one attribute of an entity with a smartagent attached was the color blue, then a smart agent for “blue” couldhave as its attributes all the users who have the attribute blue.Another attribute could be a paint manufacturer's paint formula code forthe blue. Accessing the blue smart agent would get you links immediatelyto every other smart agent describing itself as blue.

Attributes can be independently variable, fixed, or programmable. Theoperational status (on/off) of a device can be an independent variablereportable as an attribute. As are ambient temperature, noise,vibration, load, voltage, fuel level, service age. The model and serialnumber of a device can be a fixed attribute, as are assigned location,color, weight, specifications. A programmable attribute can be likestart/stop, accelerate/decelerate, inflate/deflate, heat/cool.

Not all attributes need to be communicated. It may be safe or reasonableto assume or interpolate. For example, to limit communications bandwidthdemands.

A piece of machinery can be a “thing” in the Internet-of-Things. Suchcould be equipped with appropriate sensors to measure ambient noise,temperature, load, output, energy consumption, vibration, etc. Measuredand logged over time these attributes will usually fall into someroutine or normal pattern of behavior. Smart agent profiles can be usedto store and characterize what is normal for its “thing”. Deviationsfrom such normalcy can spell trouble, warn of impending failure, callfor maintenance, signal intrusion, etc. The smart agent itself can bedesigned to take some kind of action, e.g., by communicating a “checkmachine” warning.

For a car, a location attribute would be an independent variablerequiring a sensor like a GPS receiver. But for a utility powertransformer, the location would normally be fixed. It could however beprogrammable, as in the case where a new utility power transformer islocated in a warehouse inventory, then assigned to be installed on autility pole, or has been removed to a hazardous waste site to have itstoxic oil reserves removed. In this later case, the smart agent could beimplemented within the utility power transformer, but would be better ina virtual location that always had power and communications to stayalive. Like an on-line dossier file.

The “thing” smart agents can comprise attributes that have corresponding“attribute” smart agents among 121-127. Discovering or broadcasting toall “thing” smart agents that share a particular attribute would beimmediately and directly accessible and quantifiable by the particular“attribute” smart agent 121-127 within server 128. For example, if theattribute were “MasterCard”, then all credit card entities or things101-105 with that attribute would be immediately accessible. In anotherexample, if the attribute were “California AKW 887”, then any automobilething 101-105 with that license plate attribute would be immediatelyaccessible and available to be queried to report its GPS location.

Text Mining is used, for example, to extract maintenance personnel notesfrom complex machinery equipment maintenance logs. Such logs assemblecomments about equipment component failures and associated repairs. Textmining and unsupervised text clustering excerpt information summarizingthe actions and components addressed to a particular machine and itsoperating cycles, maintenance schedules, periodic breakdowns, and toidentify abnormal failure rates, triggers and alerts.

Text Mining in general is an automated process of extracting somepreviously unknown information from analysis of unstructured texts. TextMining looks to be similar to Data Mining, but there are substantialdifferences. Data mining uses pattern-extraction of databases. TextMining crawls over natural language texts. Text-mining differs also fromweb-searching where the information being searched already exists. InText Mining, machines extract something new from an already availableresource. Fuzzy logic can be applied to text-mining, e.g., for documentclustering into pre-specified clusters. Documents can be classified intocategories, such as relevant and irrelevant. In a first step, documentsare cleaned of advertisements and tags. Hyphens are eliminated and stopwords are removed. Word stemming is used to represent words by theirroots. Each document can then be represented as an assembly of words.Weights are assigned to words on the basis of their respectivesignificance in the document, e.g., frequency of occurrence.

Word frequency can be calculated as:

WF=(Word Count/(Total Words in the Document))×10000;

where, “m” is the number of words are chosen and each document isrepresented as a set of “m” values which are the WF values of thosecorresponding “m” words in that document.

Fuzzy Logic and text-mining can be used to cluster similar documentstogether. Document Clustering is used by a machine to group documentsinto meaningful groups, e.g., using c-means algorithm. Such algorithmcomprises two types, Hard c-means and Fuzzy c-means (FCM). Hard c-meansalgorithms cluster “m” observations into “c” clusters. Each cluster hasa cluster center. Each observation fits into the least distant cluster.Each observation is strictly clustered into a single cluster. FCM is avariation of the hard c-means clustering algorithm. Every observationhas a membership value associated with each of the clusters which isinversely related to the distance of the observation from the center ofthe cluster.

FCM can cluster documents into a required number of categories. A prioriinformation is used to fix these clusters.

Unsupervised Clustering groups data instances together in groups basedon their similarities. Clustering is a process of organizing member intogroups that are similar in some respect. A similarity is measured by adistance related to an attribute value. Each cluster is dissimilar tothe members in other clusters. Clustering algorithms are usefullyapplied in marketing to find groups of customers with similar buyingbehaviors and other attributes that can be reported and followed.

Given a set of N items susceptible to being clustered, and an N*Ndistance (or similarity) matrix, the basic process of hierarchicalclustering involves:

-   -   1. Assigning each item to its own cluster, such that for N        items, there are N clusters, each with just one item;    -   2. A series of attempts are made to pair each cluster with other        clusters, and to merge each candidate into the one cluster.        Fuzzy logic can be used to calculate the degree of membership of        each item in other clusters;    -   3. An attribute value that represents the common values of the        members of each cluster is changed; and    -   4. Steps 2-3 are repeated until the minimum number of clusters        desired has been assembled.

Association learning is used to discover relevant associations anddependencies between entities and within an entity's behavior. It isuseful for identifying connections among elements, communication habits,and hidden relationships. Associative learning is used in marketing, infraud prevention, and Failure Prevention. For example, a loss of coolingin a machine can cause damage to several other components that will needto be targeted in a maintenance directive.

The dependencies and what always goes together can be learned by oneentity given a long time, or by many entities in parallel, e.g., warmweather means more ice cream will be sold. Analyzing billions oftransactions during long periods (months, years) with data processingsystems can discover things that are impossible for humans to seedirectly.

The digital logic table-lookup implementation of a decision tree can berealized with a programmable read only memory (PROM) device. The inputvariables are connected to the address bit inputs of the PROM. Onebinary variable each to each address bit. The memory locations that theyaddress in combination are programmed with bits that represent thedecision tree's decision for each input combination.

Case-based reasoning can similarly be implemented with digital logicdevices. For example, with content-addressable-memory (CAM). Cases,really sets of input conditions, can be matched with previous cases thatwere solved. The solutions are encoded into the CAM locations addressed.

Neural networks are implementable in digital logic devices. For example,an FPGA implementation of a multilayer perceptron neural network waspresented by D. Ferrer and published in Design, Automation and Test inEurope Conference and Exhibition, 2004. Proceedings (Volume:3). Thesystem is parameterized both in network related aspects, e.g., number oflayers and number of neurons in each layer, and implementationparameters, e.g., word width, pre-scaling factors and number ofavailable multipliers). This allows the design to be used in manydifferent network realizations, or to try different area-speedtrade-offs simply by recompiling the design. Fixed-point arithmetic withpre-scaling configurable in a per layer basis was used. The system wastested on an Altera ARC-PCI board. Several examples from differentapplication domains were implemented showing the flexibility and ease ofuse of the obtained circuit. Even with the rather old board used, anappreciable speed-up was said to be obtained compared with asoftware-only implementation based on MATLAB neural network toolbox.

Smart agents can be implemented with digital logic devices. Thisespecially makes sense when one smart agent is paired with one machineout-in-the-wild. And even more so if the machines are mass produced andparticipate in the emerging Internet-of-Things (IoT). E.g., anapplication specific integrated circuit (ASIC) having embedded writeablenon-volatile memory to store real-time, long-term, and recursive profileinformation.

FIG. 3 represents a method embodiment of the present invention formonitoring the operation of machines and for issuing calls forpreventative maintenance and predictions of equipment failures, and isreferred to herein by the general reference numeral 300. A step 302attaches monitoring devices, instruments, and transducers to a machinesubject to operational failures. A step 304 reads in the measurementsand data obtained by the monitoring devices, instruments, andtransducers regarding the status and operation of the machine. A step306 empanels a “jury” of classification models as “jurors” to assess themeasurements and data obtained. A step 308 presents all the measurementsand data obtained to the empaneled jury for each juror's consideration.

A step 310 classifies the measurements and data obtained and presentedto the jury according to a logic decision tree and outputting a jurorvote that includes a confidence assessment.

A step 312 classifies the measurements and data obtained and presentedto the jury according to a neural network and outputting another jurorvote that includes a confidence assessment.

A step 314 classifies the measurements and data obtained and presentedto the jury according to a fuzzy logic and outputting another juror votethat includes a confidence assessment.

A step 316 classifies the measurements and data obtained and presentedto the jury according to a smart agent profiling and outputting anotherjuror vote that includes a confidence assessment. Such profilingincludes short-term, long-term, and recursive profiling.

A step 318 classifies the measurements and data obtained and presentedto the jury according to business rules and outputting another jurorvote that includes a confidence assessment. “Business Rules” here caninclude actual business enterprise policies and distilled serviceexpertise.

A step 320 classifies the measurements and data obtained and presentedto the jury according to case-based reasoning and outputting anotherjuror vote that includes a confidence assessment. A “case” here could bea previous situation in which a high coolant temperature caused adeterioration of the lubricants in an engine, for example.

A step 322 collects together all the juror votes into a single “ballot”and mathematically applies individual weights in calculations to eachrespective juror vote with respect to its own confidence assessment andan a priori data input 323. The a priori data inputs 323 represent howconfident a “judge” is in each juror. In a courtroom jury, judges arenot allowed to separate the votes in a jury ballot and verdict. Nor canthey use their wisdom, experience, and training to give weight or evendismiss a juror's individual vote. In method 300 the “judge” embodied instep 322 can do all of that and more.

Step 322 includes tallying a verdict from the results obtained in theprevious steps, and that predicts an operational failure of the machinein a report it issues to service personnel and management.

Step 322 further includes tallying another verdict from the resultsobtained in the previous steps. A summons for service personnel to do aparticular service procedure and/or a replacement part for the machineis made by outputting another report.

In summary, method 300 reduces the costs of maintaining machines.

In alternative embodiments of method 300 a step can be included thatclassifies the measurements and data obtained and presented to the juryaccording to an associative-learning juror included within the jury, andthat calculates its decisions on the basis of associations betweenresponses observed at its inputs to any particular stimulus it haspreviously learned, and outputting another juror vote that includes aconfidence assessment.

In alternative embodiments of method 300 a step can be included thatclassifies the measurements and data obtained and presented to the juryaccording to clustering logic included within the jury panel, and thatcalculates its output decisions on the basis of clusters observed in theinput data, and outputting another juror vote that includes a confidenceassessment.

In alternative embodiments of method 300 a step can be included thatclassifies the measurements and data obtained and presented to the juryaccording to an optimization logic included within the jury panel, andthat calculates its output decisions on the basis which combinations ofvalues then at its inputs are constrained as allowable and whichcombinations are not, and outputting another juror vote that includes aconfidence assessment.

In alternative embodiments of method 300 a step can be included thatclassifies the measurements and data obtained and presented to the juryaccording to a text mining logic included within the jury panel, andthat bases its output decisions on an automated process of extractinginformation from analyses of unstructured texts at its inputs, andoutputting another juror vote that includes a confidence assessment.

In alternative embodiments of method 300 a step can be included that“cleans” the data between a machine monitoring device and the jurypanel, and including data-correction, data-deletion, and data-formattingto produce a single-format data structure at its output.

In alternative embodiments of method 300 a step can be included thatenriches the data between a machine monitoring device and the jurypanel, and including data-insertion, data-computation, anddata-formatting logic in combination to produce an enrichedsingle-format data structure at its output.

FIG. 4 illustrates an electronic appliance 400 for monitoring theoperation of a machine 402 and for issuing calls for preventativemaintenance and warnings of equipment failures in service reports 404. Amonitoring device 406 receives electrical signals from transducers,pickups, and meters mounted to machine 402. These continuously measure(in parallel) a variety of operational parameters of the machine. Themeasurements are each electrically represented by voltages, currents,and/or digital data so the information can be accepted and used bycombinational digital logic. A data cleanup device 408 removes noise,extraneous information, and unverified data. It further normalizes thedata into standard formats and data structures. A data enrichment device410 adds attributes to the data that can be interpreted, extrapolated,or calculated from the normalized data.

A jury panel 412 represents a digital logic assembly of several jurormodules with electrical input connections that each receive the samecleaned-up, enriched operational parameter measurements of machine 802at the same time, and each independently deliberate in solitude on anindependent juror vote. Each juror makes its decisions in a differentway and communicates as a juror-vote in a digital output.

A smart-agent juror module 414 is included within the jury panel 412 andincludes a real-time profiling logic and storage memory, a long-termprofiling logic and storage memory, and a recursive profiling logic andstorage memory, in parallel between its inputs and outputs.

A data-mining juror module 416 is included within the jury panel 412 andincludes a logic decision tree between its inputs and its outputs.

A neural-network juror module 418 is included within jury panel 412 andincludes neural network logic between its inputs and its outputs.

A fuzzy logic juror module 420 is included within jury panel 412 andincludes fuzzy logic between its inputs and its outputs to automaticallycluster information into a plurality of risk categories and decreasessensitivity to noisy or outlier data.

A business-rules juror module 422 is included within jury panel 412 andincludes business rules logic between its inputs and its outputs.

A case-based reasoning juror module 424 is included within jury panel412 and includes representations of previous case sets of operationalparameters of similar machines and past decisions determined to besuccessful ones, and a comparator to match these previous cases to acurrent set of operational parameters of the machine provided at itsinputs, and an output for its conclusions.

An associative-learning juror module 426 is included within jury panel412 and calculates its output decisions on the basis of associationsbetween responses observed at its inputs to any particular stimulus ithas previously learned.

A clustering juror module 428 is included within jury panel 412 andincludes clustering logic between its inputs and its outputs.

Other types of classification model jurors 430 can be added to the jurypanel 412 to add their unique perspectives to a diverse jury panel.

A metarule arbiter module 432 includes several weight calculators thateach respectively receive a corresponding one of the independentjuror-vote digital outputs. Such further includes a digital summationdevice that tallies a single composite prediction output.

An output device 434 transforms the composite prediction output into ahuman-readable form.

The other types of classification model jurors 430 mentioned can includean optimization-suite juror module included with the jury panel, andthat includes optimization logic that specifies which combinations ofvalues then at its inputs are constrained as allowable and whichcombinations are not, and that produces its decision at its outputs asdigital data.

The other types of classification model jurors 430 mentioned can includea text mining juror module is included within jury panel 412 andincludes text mining logic between its inputs and its outputs as digitaldata.

The other types of classification model jurors 430 mentioned can includea data cleaning device connected between the machine monitoring deviceand the jury panel, and that includes data-correction, data-deletion,and data-formatting logic that combine to produce a single-format datastructure at its output.

The other types of classification model jurors 430 mentioned can includegenetic algorithm component logic.

The other types of classification model jurors 430 mentioned can includephonetic algorithm component logic. and

The other types of classification model jurors 430 mentioned can includefield machine algorithm component logic.

The data enrichment device 410 connected between the machine monitoringdevice 406 and the jury panel 412 further can include includesdata-insertion, data-computation, and data-formatting logic incombination to produce an enriched single-format data structure at itsoutput.

The other types of classification model jurors 430 mentioned can includea velocity-analyzer juror module is included within jury panel 412 andincludes velocity analyzing and data compression logic between itsinputs and its outputs.

The other types of classification model jurors 430 mentioned can includean optimization suite juror module is included within jury panel 412 andincludes optimization logic that specifies which combinations of valuesthen at its inputs are constrained as allowable and which combinationsare not, and that produces its decision at its outputs.

Although particular embodiments of the present invention have beendescribed and illustrated, such is not intended to limit the invention.Modifications and changes will no doubt become apparent to those skilledin the art, and it is intended that the invention only be limited by thescope of the appended claims.

1. A method for monitoring the operation of machines and for issuingcalls for preventative maintenance and predictions of equipmentfailures, comprising: attaching monitoring devices, instruments, andtransducers to a machine subject to operational failures; reading inmeasurements and data obtained by the monitoring devices, instruments,and transducers regarding the status and operation of the machine;empanelling a jury of classification models as jurors to assess themeasurements and data obtained with a separate computer programmed forthat purpose; presenting all the measurements and data obtained to thejury with a separate computer programmed for that purpose; classifyingthe measurements and data obtained and presented to the jury accordingto a logic decision tree and outputting a juror vote that includes aconfidence assessment with a separate computer programmed for thatpurpose; classifying the measurements and data obtained and presented tothe jury according to a neural network and outputting another juror votethat includes a confidence assessment with a separate computerprogrammed for that purpose; classifying the measurements and dataobtained and presented to the jury according to a fuzzy logic andoutputting another juror vote that includes a confidence assessment witha separate computer programmed for that purpose; classifying themeasurements and data obtained and presented to the jury according to asmart agent profiling and outputting another juror vote that includes aconfidence assessment with a separate computer programmed for thatpurpose; classifying the measurements and data obtained and presented tothe jury according to business rules and outputting another juror votethat includes a confidence assessment with a separate computerprogrammed for that purpose; classifying the measurements and dataobtained and presented to the jury according to case-based reasoning andoutputting another juror vote that includes a confidence assessment witha separate computer programmed for that purpose; collecting all thejuror votes into a single ballot and mathematically apply individualweights in calculations to each respective juror vote with respect toits own confidence assessment and a priori data inputs with a separatecomputer programmed for that purpose; tallying a verdict from theresults obtained in the previous steps, and that predicts an operationalfailure of the machine by outputting a report with a separate computerprogrammed for that purpose; and tallying another verdict from theresults obtained in the previous steps, and that summons a particularservice procedure and/or a replacement part for the machine byoutputting another report so the costs of maintaining the machine arereduced.
 29. The method of claim 1, further comprising: classifying themeasurements and data obtained and presented to the jury according to anassociative-learning juror included within the jury, and that calculatesits decisions on the basis of associations between responses observed atits inputs to any particular stimulus it has previously learned, andoutputting another juror vote that includes a confidence assessment. 3.The method of claim 1, further comprising: classifying the measurementsand data obtained and presented to the jury according to clusteringlogic included within the jury panel, and that calculates its outputdecisions on the basis of clusters observed in the input data, andoutputting another juror vote that includes a confidence assessment. 4.The method of claim 1, further comprising: classifying the measurementsand data obtained and presented to the jury according to an optimizationlogic included within the jury panel, and that calculates its outputdecisions on the basis which combinations of values then at its inputsare constrained as allowable and which combinations are not, andoutputting another juror vote that includes a confidence assessment. 5.The method of claim 1, further comprising: classifying with a separatecomputer programmed for that purpose the measurements and data obtainedand presented to the jury according to a text mining logic includedwithin the jury panel, and that bases its output decisions on anautomated process of extracting information from analyses ofunstructured texts at its inputs, and outputting another juror vote thatincludes a confidence assessment.
 6. The method of claim 1, furthercomprising: data cleaning between a machine monitoring device and thejury panel, and including data-correction, data-deletion, anddata-formatting to produce a single-format data structure at its output.7. The method of claim 1, further comprising: data enriching between amachine monitoring device and the jury panel, and includingdata-insertion, data-computation, and data-formatting logic incombination to produce an enriched single-format data structure at itsoutput.
 8. A group of electronic appliances for monitoring the operationof machines and for issuing calls for preventative maintenance andpredictions of equipment failures, comprising: a monitoring deviceattached by instruments to a machine and having electrical inputs andtransducers that measure in parallel a variety of operational parametersof the machine and that are then electrically represented by voltages,currents, and/or digital data; a jury panel comprising a digital logicassembly of several juror modules with electrical input connections thateach receive the same operational parameter measurements of the machineat the same time, and that each produce an independent juror votedecided in a different way and communicated as a juror-vote digitaloutput; a smart-agent juror module included with the jury panel and thatincludes a real-time profiling logic and storage memory, a long-termprofiling logic and storage memory, and a recursive profiling logic andstorage memory, in parallel between its inputs and outputs; adata-mining juror module included with the jury panel and that includesa logic decision tree between its inputs and its outputs; aneural-network juror module included with the jury panel and thatincludes neural network logic between its inputs and its outputs; afuzzy logic juror module included with the jury panel and that includesfuzzy logic between its inputs and its outputs to automatically clusterinformation into a plurality of risk categories and decreasessensitivity to noisy or outlier data; a business-rules juror moduleincluded with the jury panel and that includes business rules logicbetween its inputs and its outputs; a case-based reasoning juror moduleincluded with the jury panel and that includes representations ofprevious case sets of operational parameters of similar machines andpast decisions determined to be successful ones, and a comparator tomatch these previous cases to a current set of operational parameters ofthe machine provided at its inputs, and an output for its conclusions;an associative-learning juror module included with the jury panel, andthat calculates its output decisions on the basis of associationsbetween responses observed at its inputs to any particular stimulus ithas previously learned; a clustering juror module included with the jurypanel and that includes clustering logic between its inputs and itsoutputs; a metarule arbiter module comprising several weight calculatorseach respectively attached to receive a corresponding one of saidindependent juror-vote digital outputs, and a digital summation devicethat produces a single composite prediction output from a set of severalweighted sums of said dependent juror vote digital outputs; and anoutput device that transforms the composite prediction output intohuman-readable form.
 9. The group of electronic appliances of claim 8,further comprises: an optimization-suite juror module included with thejury panel, and that includes optimization logic that specifies whichcombinations of values then at its inputs are constrained as allowableand which combinations are not, and that produces its decision at itsoutputs as digital data.
 10. The group of electronic appliances of claim8, further comprises: a text mining juror module included with the jurypanel and that includes text mining logic between its inputs and itsoutputs as digital data.
 11. The group of electronic appliances of claim8, further comprises: a data cleaning device connected between themachine monitoring device and the jury panel, and that includesdata-correction, data-deletion, and data-formatting logic that combineto produce a single-format data structure at its output.
 12. The groupof electronic appliances of claim 8, further comprises: a dataenrichment device connected between the machine monitoring device andthe jury panel, and that includes data-insertion, data-computation, anddata-formatting logic in combination to produce an enrichedsingle-format data structure at its output.
 13. The group of electronicappliances of claim 8, further comprises: a velocity-analyzer jurormodule included with the jury panel and that includes velocity analyzingand data compression logic between its inputs and its outputs.
 14. Thegroup of electronic appliances of claim 8, further comprises: anoptimization suite juror module included with the jury panel and thatincludes optimization logic that specifies which combinations of valuesthen at its inputs are constrained as allowable and which combinationsare not, and that produces its decision at its outputs.